As Salt, Jordan

Al-Ahliyya Amman University
As Salt, Jordan

Al-Ahliyya Amman University is located in Amman, Jordan. Founded in 1990, it was the first private university in Jordan. The university is accredited by the Ministry of Higher Education and Scientific Research, Jordan, and is a member of four university associations. Foreign students come from a diversity of countries, for example Syria, Iraq, the United States, Japan and Israel. Wikipedia.

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Al Sharabati T.,Al-Ahliyya Amman University
Journal of Communications Software and Systems | Year: 2016

In this paper, the effects of intersystem cross correlation of 3GPP user' codes to GPS satellites' codes will be demonstrated. The investigation and analysis are in the form of cross correlation between 3GPP users' codes and GPS satellites Pseudo Random Noise (PRN) sequences. The investigation and analysis will involve the similarities in generation and system architecture of both the 3GPP user' codes and GPS satellites' codes. The extent of intersystem interference will be displayed in the form of results for cross correlation, correlation coefficient, and signal to noise ratio. Recommendations will be made based on the results. © 2016 CCIS.

Shambour Q.,Al-Ahliyya Amman University | Lu J.,University of Technology, Sydney
Journal of Computer and System Sciences | Year: 2015

Although Collaborative Filtering (CF)-based recommender systems have received great success in a variety of applications, they still under-perform and are unable to provide accurate recommendations when users and items have few ratings, resulting in reduced coverage. To overcome these limitations, we propose an effective hybrid user-item trust-based (HUIT) recommendation approach in this paper that fuses the users' and items' implicit trust information. We have also considered and computed user and item global reputations into this approach. This approach allows the recommender system to make an increased number of accurate predictions, especially in circumstances where users and items have few ratings. Experiments on four real-world datasets, particularly a business-to-business (B2B) case study, show that the proposed HUIT recommendation approach significantly outperforms state-of-the-art recommendation algorithms in terms of recommendation accuracy and coverage, as well as significantly alleviating data sparsity, cold-start user and cold-start item problems. © 2015 Elsevier Inc.

AL-Tahrawi M.,Al-Ahliyya Amman University
UPB Scientific Bulletin, Series C: Electrical Engineering | Year: 2015

Feature Selection (FS) is a crucial preprocessing step in Text Classification (TC) systems. FS can be either Class-Based or Corpus-Based. Polynomial Network (PN) classifiers have proved recently to be competitive in TC using a very small subset of corpora features. This paper presents an empirical study of the performance of PN classifiers using Aggressive Class-Based FS. Seven of the stateof- the art FS metrics are experimented and compared: Chi Square (CHI), Information Gain (IG), Odds Ratio (OR), GSS, NGL coefficient, Document Frequency (DF), and Gain Ratio (GR).The study is conducted on the Reuters Benchmark Corpus. Experimental results are presented in terms of both micro-averaged and macro-averaged precision, recall and F measures. Results reveal that aggressive Class-Based Chi-Square and DF metrics work best for Reuters using PN classifiers compared to the other five FS metrics experimented in this research.

Hassan M.,Al-Ahliyya Amman University
International Arab Journal of Information Technology | Year: 2013

Current Internet trends are moving towards decentralization of computation, storage, and resources. Supporting network management for such a vast and a highly complex system has become a challenging issue. A management platform has to sufficiently support decentralization, collaboration, and integration. Grid technologies have the potential to serve as management architecture due to the support of the above features. In this paper, we developed a collaborative network management architecture leveraging the key features of grid technology. Benefiting from this integration, we were able to show that multiple management tasks can be integrated and completed in parallel. This assures the management scalability and efficiency. We also showed that the management information at different networking domains can freely consume the computational resources provided through the grid interface while being executed. Grid interface has guaranteed scalability and reliability for the network management tasks. We have simulated the system prototype and closely studied its efficiency.

Abu-Shikhah N.,Al-Ahliyya Amman University | Elkarmi F.,Al-Ahliyya Amman University
Energy | Year: 2011

Medium-term load forecasting is an important stage in electric power system planning and operation. It is used in maintenance scheduling, and to plan for outages and major works in the power system. A new technique is proposed which uses hourly loads of successive years to predict hourly loads and peak load for the next selected time span. The proposed method implements a new combination of some existing and well established techniques. This is done by first filtering out the load trend, then applying the SVD (singular value decomposition) technique to de-noise the resulting signal. Hourly load is thus divided to three main components: a) a load trend-following component, b) a random component, and c) a de-noised component. Results of applying the technique to the Jordanian power system showed that good forecasting accuracies are attained. In addition, the proposed method outperforms the traditional exponential curve fitting method. The peak load error was found to be less than 5% using the proposed methodology. It was also found that a lag period of 4 years suits the load forecasting purposes of the Jordanian power system. The proposed method is generic and can be implemented to the hourly loads of any power system. © 2011 Elsevier Ltd.

Musmar M.A.,Al-Ahliyya Amman University
Jordan Journal of Civil Engineering | Year: 2013

The use of shear wall-buildings is quite common in some earthquake prone regions. During seismic excitation, they contribute in absorbing moments and shear forces and reduce torsional response. Usually, architectural design leads to the existence of doors and windows within shear walls. Previous researches on the behavior of shear walls with openings assumed elastic analysis utilizing shell and brick elements. The present work adopts nonlinear finite element analysis using solid65 element. The analysis comprises both material and geometric nonlinearities. Solid65 element models the nonlinear response of concrete material based on a constitutive model for the triaxial behavior of concrete after Williams and Warnke. Five shear wall models with different opening sizes are analyzed. A sixth model of a solid shear wall is also presented to compare the analysis results. The paper studies the effect of the size of the openings on the behavior of the reinforced concrete shear walls. The study indicates that openings of small dimensions yield minor effects on the response of shear walls with respect to both normal stresses along the base level of shear walls and maximum drift. Cantilever behavior similar to that of a solid shear wall takes place and analogous to that of coupled shear walls. On the other hand, when openings are large enough, shear walls behave as connected shear walls, exhibiting frame action behavior. © 2013 JUST. All Rights Reserved.

Al-Tahrawi M.M.,Al-Ahliyya Amman University
International Journal of Intelligent Systems | Year: 2014

The significance of low frequent terms in text classification (TC) was always debatable. These terms were often accused of adding noise to the TC process. Nevertheless, some recent studies have proved that they are very helpful in improving the performance of text classifiers. This paper shows the significance of low frequent terms in enhancing the performance of English TC, in terms of precision, recall, F-measure, and accuracy. Six well-known TC algorithms are tested on the benchmark Reuters Data Set, once keeping low frequent terms and another time discarding them. These algorithms are the support vector machines, logistic regression, k-nearest neighbor, naive bayes, the radial basis function networks, and polynomial networks. All the experiments in this research have shown a superior performance of TC when the low frequent terms are used in classification. © 2014 Wiley Periodicals, Inc.

Alnazly E.K.,Al-Ahliyya Amman University
Hemodialysis International | Year: 2016

Recent studies reported hemodialysis patients' sufferings from physical and psychosocial issues, but few studies reported family-caregiver burdens. This study aims to explore the burdens and coping strategies of caregivers of patients receiving hemodialysis. Caregivers of patients undergoing hemodialysis (n=139) at 3 dialysis units were given 3 forms: Caregiver and Patient Characteristics, Oberst Caregiving Burden Scale Difficulty Subscale, and Ways of Coping Questionnaire. Descriptive statistics, correlational analysis, and multiple regression analysis were performed. The Oberst Caregiving Burden Scale was significantly related to self-controlling (r=0.20) and seeking social support (r=0.17). Caregiver burden was positively and significantly correlated with self-controlling coping subscale, with t=1.10, P=0.05, and β=0.25. Living with the patient was the only variable that was a significant predictor of burden, with t=2.96, P=0.00, and β=0.331. Living with patients predicted caregiver burden, and the burden scale correlated with self-controlling. The findings contribute to the evidence on the adverse health effects of caregivers of patients receiving hemodialysis. This study suggests that nursing interventions should target caregiver knowledge for better coping. © 2016 International Society for Hemodialysis.

Fraihat S.,Al-Ahliyya Amman University
International Journal of Innovative Computing, Information and Control | Year: 2016

Automatic document classification has become increasingly important and difficult due to the large scale of the electronic documents used in the last years. Traditional information retrieval systems are based on the extraction of keywords from documents; these keywords serve as a basis for documents classification. This paper proposes a new semantic approach for documents classification. Specifically, our approach captures, in addition to the keywords frequency, the meaning of these keywords in documents using domain ontology. The main idea is to represent documents by concepts rather than keywords, and calculates weights for these concepts to reflect their importance in the documents where they appear. The presence of concepts in the same paragraph, section, document, or document set, provides important information to better extract and understand the semantic content of the document and therefore improves its classification. The experimental evaluation is carried out using the Reuters document collection RCV1-v2 and the GALEN medical ontology. The documents are classified using the SVM classifier. The experimental results demonstrate that the proposed approach yields higher accuracy, precision and recall compared to the traditional keyword-based information retrieval approaches. © 2016, IJICIC Editorial Office. All rights reserved.

Abbas M.A.,Al-Ahliyya Amman University
Journal of Steroid Biochemistry and Molecular Biology | Year: 2016

Adipose tissue has long been identified as the major site of vitamin D storage. Recent studies have demonstrated that VDR and vitamin D metabolizing enzymes are expressed in adipocytes. Furthermore, it has been shown that vitamin D regulates adipogenic gene expression as well as adipocyte apoptosis. Vitamin D is active in adipocytes at all levels. It interacts with membrane receptors, adaptor molecules, and nuclear coregulator proteins. Several functions of unliganded nVDR were discovered by studying human samples from patients having hereditary vitamin D resistant rickets, transgenic mice overexpressing the VDR and VDR knockout mice. Through its genomic action, vitamin D participates in the regulation of energy metabolism by controlling the expression of uncoupling proteins. In vitro, vitamin D stimulates lipogenesis and inhibits lipolysis by interacting with mVDR. mVDR is present in caveolae of the plasma membrane and is the same as the classic nVDR. In addition, vitamin D affects directly the expression of the appetite regulating hormone, leptin. Some researchers reported also that vitamin D regulates the expression of the insulin sensitizing hormone, adiponectin. Vitamin D reduced cytokine release and adipose tissue inflammation through the inhibition of NF-κB signaling. Scientific research investigating the role of adipose tissue resident immune cells in the pathogenesis of obesity-associated inflammation is scarce. Obesity is associated with vitamin D deficiency. However there is no scientific evidence to prove that vitamin D deficiency predispose to obesity. Vitamin D supplementation may prevent obesity but it does not lead to weight loss in obese subjects. © 2016 Elsevier Ltd.

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